Applications & Services

A number of mapping services have been developed in order to serve the public user. One Important component of European Soil Data Centre is the Map Viewer which is a web-based application that allows the user to navigate the European Soil Database and other important data layers hosted in the European Soil Data Center(ESDAC).

The Map viewers have also been configured as Web Map Service (WMS), a feature that allows WMS clients to request map layers from the European Soil Portal which can then be combined with layers from other WMS servers, located in other parts of the world. In order to guarantee interoperability, the developed services are based on international standards, as promoted by the INSPIRE initiative.

In the Soils Portal, WMS services have been developed in the context of INSPIRE , compliant with INSPIRE principles and Open GIS Consortiumstandards. In practice, this means that the SOMIS (Soil database attribute), PESERA (Soil Erosion) and OCTOP (Organic Carbon) layers can be viewed through any Web Mapping Service Client (WMS Standalone or Web viewer, ESRI ArcGIS). Metadata are also Available.

The ESDAC Map Viewer:

allows the user to navigate key soil data for Europe. It provides access to the attributes of the European Soil Database and some additional data related to main soil threats as identified in the Soil Thematic Strategy. The ESDAC Map Viewer is developed according to standards (OGC WMS) so that they are interoperable with similar information allowing real-time integration of environmental data from around the world.

The Viewer integrates the European Soil Database layers and some other soil layers in one single web-based application. You may navigate and select each of the:

Maps of preservation capacity of cultural artefacts and buried materials in soils in the EU: Strati

Maps (2016) that indicate the preservation capacity of cultural artefacts and buried materials in soils in the EU, for bones, teeth and shells (bones), organic materials (organics), metals (Cu,...

Full Abstract

Maps (2016) that indicate the preservation capacity of cultural artefacts and buried materials in soils in the EU, for bones, teeth and shells (bones), organic materials (organics), metals (Cu, bronze and Fe) (metals), stratigraphic evidence (strati).
This is the Strati layer.

2017-09

Maps of preservation capacity of cultural artefacts and buried materials in soils in the EU: Bones

Maps (2016) that indicate the preservation capacity of cultural artefacts and buried materials in soils in the EU, for bones, teeth and shells (bones), organic materials (organics), metals (Cu,...

Full Abstract

Maps (2016) that indicate the preservation capacity of cultural artefacts and buried materials in soils in the EU, for bones, teeth and shells (bones), organic materials (organics), metals (Cu, bronze and Fe) (metals), stratigraphic evidence (strati).
This is the Bones layer

2017-09

Maps of preservation capacity of cultural artefacts and buried materials in soils in the EU: Metals

Maps (2016) that indicate the preservation capacity of cultural artefacts and buried materials in soils in the EU, for bones, teeth and shells (bones), organic materials (organics), metals (Cu,...

Full Abstract

Maps (2016) that indicate the preservation capacity of cultural artefacts and buried materials in soils in the EU, for bones, teeth and shells (bones), organic materials (organics), metals (Cu, bronze and Fe) (metals), stratigraphic evidence (strati).
This is the Metals layer.

2017-09

Maps of preservation capacity of cultural artefacts and buried materials in soils in the EU: Organics

Maps (2016) that indicate the preservation capacity of cultural artefacts and buried materials in soils in the EU, for bones, teeth and shells (bones), organic materials (organics), metals (Cu,...

Full Abstract

Maps (2016) that indicate the preservation capacity of cultural artefacts and buried materials in soils in the EU, for bones, teeth and shells (bones), organic materials (organics), metals (Cu, bronze and Fe) (metals), stratigraphic evidence (strati).
This layer is for organic materials

2017-08

N2O emissions from agricultural soils in Europe: MT2 model

This dataset derives from the integration of the LUCAS soil survey with the bio-geochemistry process-based model DayCent. The model was ran for more than 11,000 LUCAS sampling points under...

Full Abstract

This dataset derives from the integration of the LUCAS soil survey with the bio-geochemistry process-based model DayCent. The model was ran for more than 11,000 LUCAS sampling points under agricultural use, assessing also the model uncertainty. Meta-models based on model outcomes and the Random Forest algorithm were used to upscale the N2O emissions at 1km resolution.
MT2 meta-model

2017-08

N2O emissions from agricultural soils in Europe: MT1 model

This dataset derives from the integration of the LUCAS soil survey with the bio-geochemistry process-based model DayCent. The model was ran for more than 11,000 LUCAS sampling points under...

Full Abstract

This dataset derives from the integration of the LUCAS soil survey with the bio-geochemistry process-based model DayCent. The model was ran for more than 11,000 LUCAS sampling points under agricultural use, assessing also the model uncertainty. Meta-models based on model outcomes and the Random Forest algorithm were used to upscale the N2O emissions at 1km resolution.
MT1 meta-model

2017-08

Soil Biomass Productivity maps of grasslands and pasture, of croplands and of forest areas in the European Union (EU27): croplands

This dataset consists of 3 GIS maps that indicate the soil biomass productivity of grasslands and pasture, of croplands and of forest areas in the European Union (EU27): this layer displays Soil...

Full Abstract

This dataset consists of 3 GIS maps that indicate the soil biomass productivity of grasslands and pasture, of croplands and of forest areas in the European Union (EU27): this layer displays Soil biomass productivity of croplands, values normalized to a range [0..10]

Three major components of soil biodiversity are assesed: a) soil microorganisms, b) fauna, and c) biological functions. The maps were developed based on 13 potential threats to soil biodiversity...

Full Abstract

Three major components of soil biodiversity are assesed: a) soil microorganisms, b) fauna, and c) biological functions. The maps were developed based on 13 potential threats to soil biodiversity which were proposed to experts with different backgrounds in order to assess biodiversity threat.

2017-07

Potential threats to soil biodiversity in Europe: Fauna

Three major components of soil biodiversity are assesed: a) soil microorganisms, b) fauna, and c) biological functions. The maps were developed based on 13 potential threats to soil biodiversity...

Full Abstract

Three major components of soil biodiversity are assesed: a) soil microorganisms, b) fauna, and c) biological functions. The maps were developed based on 13 potential threats to soil biodiversity which were proposed to experts with different backgrounds in order to assess biodiversity threat.

2017-07

Potential threats to soil biodiversity in Europe: Microorganisms

Three major components of soil biodiversity are assessed: a) soil microorganisms, b) fauna, and c) biological functions. The maps were developed based on 13 potential threats to soil biodiversity...

Full Abstract

Three major components of soil biodiversity are assessed: a) soil microorganisms, b) fauna, and c) biological functions. The maps were developed based on 13 potential threats to soil biodiversity which were proposed to experts with different backgrounds in order to assess biodiversity threat.

2017-05

Dominant Soil Typological Unit (STU), WRBLV1

Soil Reference Group code of the STU from the World Reference Base for Soil Resources.

Full Abstract

Soil Reference Group code of the STU from the World Reference Base for Soil Resources.

2017-05

Dominant Soil Typological Unit (STU), WRBFU

Full soil code of the STU from the World Reference Base for Soil Resources

Full Abstract

Full soil code of the STU from the World Reference Base for Soil Resources

2017-01

Global Rainfall Erosivity

Rainfall erosivity dataset (2017) is one of the input layers when calculating the Revised Universal Soil Loss Equation (RUSLE) model, which is the most frequently used model for soil erosion risk...

Full Abstract

Rainfall erosivity dataset (2017) is one of the input layers when calculating the Revised Universal Soil Loss Equation (RUSLE) model, which is the most frequently used model for soil erosion risk estimation; for the whole World; R-factor map at resolutions of 30 arc-sec ((~1 km at the Equator).

2016-01

Soil Biomass Productivity maps of grasslands and pasture, of croplands and of forest areas in the European Union (EU27): forest areas

This dataset consists of 3 GIS maps that indicate the soil biomass productivity of grasslands and pasture, of croplands and of forest areas in the European Union (EU27); this layer displays Soil...

Full Abstract

This dataset consists of 3 GIS maps that indicate the soil biomass productivity of grasslands and pasture, of croplands and of forest areas in the European Union (EU27); this layer displays Soil biomass productivity of forest areas, values normalized to a range [0..10]

Predicted distribution of SOC content in Europe (based on LUCAS, BioSoil and CZO) in the context of the EU-funded SoilTrEC project: SOC Lucas + CZO

These maps of predicted distribution of SOC content in Europe (2016) are based on aggregated 23,835 soil samples collected from the LUCAS Project (samples from agricultural soil), BioSoil Project (...

Full Abstract

These maps of predicted distribution of SOC content in Europe (2016) are based on aggregated 23,835 soil samples collected from the LUCAS Project (samples from agricultural soil), BioSoil Project (samples from forest soil), and Soil Transformations in European Catchments (SoilTrEC) Project (samples from local soil data coming from five different critical zone observatories (CZOs) in Europe)"
This layer is SOC Lucas + CZO

2016-01

Soil Organic Carbon - Saturation Capacity in Europe

This dataset (map) shows the Soil Organic Carbon (SOC) saturation capacity, expressed as the ratio between the actual and the potential SOC stock in each pixel. Values close to 0 indicate a great...

Full Abstract

This dataset (map) shows the Soil Organic Carbon (SOC) saturation capacity, expressed as the ratio between the actual and the potential SOC stock in each pixel. Values close to 0 indicate a great potential of soil to store more carbon.
The actual SOC stock was derived from the Pan-European simulation using the biogeochemical CENTURY model (a detailed explanation can be found in the references below). The associated data can be found in ESDAC: "Pan-European SOC stock of agricultural soils"
The potential SOC stock was obtained simulating a grassland land use without nitrogen limitation, since it was considered a good scenario for SOC accumulation. The scenario set-up was analogous to that described in Lugato et al (2014b, see below) for the grassland land use, namely ‘AR_GR_LUC’. However to obtain a potential SOC stock, the model was ran for 2000 years with repeated actual climate, in order to reach an equilibrium condition. The simulation involved only the agricultural soils, according to the Corine Land Cover. A value of 1 was arbitrary attributed to forest soils.
The data come as a single ESRI Grid (250m resolution) in the ETRS_LAEA_10_52 Coordinate System + the two referenced articles

2016-01

Soil Organic Carbon (SOC) Projections for Europe: hd60

This dataset consists of a number of data layers (raster GRID maps) that are associated to the peer-reviewed publication "Assessment of soil organic carbon stocks under future climate and land...

Full Abstract

This dataset consists of a number of data layers (raster GRID maps) that are associated to the peer-reviewed publication "Assessment of soil organic carbon stocks under future climate and land cover changes in Europe" . Layers cover the current Soil Organic Carbon Stocks (2016) and the projected Soil Organic Carbon Stocks by 2050, for various Climate Scenarios (CCSM4, HadGEM2-AO , IPSL-CM5A-LR MRI-CGCM3) and Representative Concentration Pathways (RCPs).
hd60: HadGEM2-AO, RCP 6

2016-01

Maps of the Storing and Filtering Capacity of Soils in Europe: cation filtering capacity (FILT_CAPCA)

Soil hydraulic properties maps (2016) for Europe: for Water retention of topsoil: saturated water content (cm3/cm3), water content at field capacity (cm3/cm3), water content at wilting point (cm3/cm3...

Full Abstract

Soil hydraulic properties maps (2016) for Europe: for Water retention of topsoil: saturated water content (cm3/cm3), water content at field capacity (cm3/cm3), water content at wilting point (cm3/cm3); for Hydraulic conductivity of topsoil: saturated hydraulic conductivity (cm/day). Besides the true values in the units mentioned values scaled between 1 and 10 without measurement units were also calculated. This layer shows the 'true values' for the saturated hydraulic conductivity.

These maps of predicted distribution of SOC content in Europe (2016) are based on aggregated 23,835 soil samples collected from the LUCAS Project (samples from agricultural soil), BioSoil Project (...

Full Abstract

These maps of predicted distribution of SOC content in Europe (2016) are based on aggregated 23,835 soil samples collected from the LUCAS Project (samples from agricultural soil), BioSoil Project (samples from forest soil), and Soil Transformations in European Catchments (SoilTrEC) Project (samples from local soil data coming from five different critical zone observatories (CZOs) in Europe).
This layer is SOC LUCAS + CZO + Biosoil

2016-01

Soil Organic Carbon (SOC) Projections for Europe: hd85

This dataset consists of a number of data layers (raster GRID maps) that are associated to the peer-reviewed publication "Assessment of soil organic carbon stocks under future climate and land...

Full Abstract

This dataset consists of a number of data layers (raster GRID maps) that are associated to the peer-reviewed publication "Assessment of soil organic carbon stocks under future climate and land cover changes in Europe" . Layers cover the current Soil Organic Carbon Stocks (2016) and the projected Soil Organic Carbon Stocks by 2050, for various Climate Scenarios (CCSM4, HadGEM2-AO , IPSL-CM5A-LR MRI-CGCM3) and Representative Concentration Pathways (RCPs).
hd85: HadGEM2-AO, RCP 8.5

2016-01

Soil Biomass Productivity maps of grasslands and pasture, of croplands and of forest areas in the European Union (EU27): grasslands and pastures

This dataset consists of 3 GIS maps that indicate the soil biomass productivity of grasslands and pasture, of croplands and of forest areas in the European Union (EU27); this layer displays Soil...

Full Abstract

This dataset consists of 3 GIS maps that indicate the soil biomass productivity of grasslands and pasture, of croplands and of forest areas in the European Union (EU27); this layer displays Soil biomass productivity of grasslands and pastures, values normalized to a range [0..10]

2016-01

Maps of indicators of soil hydraulic properties for Europe: water content at field capacity

Soil hydraulic properties maps (2016) for Europe: for Water retention of topsoil: saturated water content (cm3/cm3), water content at field capacity (cm3/cm3), water content at wilting point (cm3/cm3...

Full Abstract

Soil hydraulic properties maps (2016) for Europe: for Water retention of topsoil: saturated water content (cm3/cm3), water content at field capacity (cm3/cm3), water content at wilting point (cm3/cm3); for Hydraulic conductivity of topsoil: saturated hydraulic conductivity (cm/day). Besides the true values in the units mentioned values scaled between 1 and 10 without measurement units were also calculated. This layer shows the 'true values' for the water content at field capacity.

2016-01

Global Soil Biodiversity Atlas Maps: soil biodiversity

The Soil Biodiversity map showing a simple index describing the potential level of diversity living in soils (with the use of two other datasets: distribution of microbial soil carbon used as a proxy...

Full Abstract

The Soil Biodiversity map showing a simple index describing the potential level of diversity living in soils (with the use of two other datasets: distribution of microbial soil carbon used as a proxy for soil microbial diversity, and the distribution of the main groups of soil macrofauna used as a proxy for soil fauna diversity.

2016-01

Maps of indicators of soil hydraulic properties for Europe: saturated water content

Soil hydraulic properties maps (2016) for Europe: for Water retention of topsoil: saturated water content (cm3/cm3), water content at field capacity (cm3/cm3), water content at wilting point (cm3/cm3...

Full Abstract

Soil hydraulic properties maps (2016) for Europe: for Water retention of topsoil: saturated water content (cm3/cm3), water content at field capacity (cm3/cm3), water content at wilting point (cm3/cm3); for Hydraulic conductivity of topsoil: saturated hydraulic conductivity (cm/day). Besides the true values in the units mentioned values scaled between 1 and 10 without measurement units were also calculated. This layer shows the 'true values' for the saturated water content.

2016-01

Global Soil Biodiversity Atlas Maps: soil threats

The Soil Biodiversity threats showing the potential rather than the actual level of threat to soil organisms. For the development of this map, a number of diverse threats and corresponding proxies...

Full Abstract

The Soil Biodiversity threats showing the potential rather than the actual level of threat to soil organisms. For the development of this map, a number of diverse threats and corresponding proxies were chosen

2016-01

Maps indicating the availability of Raw Material from soils in the European Union: organic soil material

This dataset (maps) indicates the availability of Raw Material (organic soil material and soil material for constructions) from soils in the European Union, and corresponds to the figures 7a and 7b...

This dataset consists of a number of data layers (raster GRID maps) that are associated to the peer-reviewed publication "Assessment of soil organic carbon stocks under future climate and land...

Full Abstract

This dataset consists of a number of data layers (raster GRID maps) that are associated to the peer-reviewed publication "Assessment of soil organic carbon stocks under future climate and land cover changes in Europe" . Layers cover the current Soil Organic Carbon Stocks (2016) and the projected Soil Organic Carbon Stocks by 2050, for various Climate Scenarios (CCSM4, HadGEM2-AO , IPSL-CM5A-LR MRI-CGCM3) and Representative Concentration Pathways (RCPs).
hd26: HadGEM2-AO, RCP 2.6

2016-01

Maps of indicators of soil hydraulic properties for Europe: water content at wilting point

Soil hydraulic properties maps (2016) for Europe: for Water retention of topsoil: saturated water content (cm3/cm3), water content at field capacity (cm3/cm3), water content at wilting point (cm3/cm3...

Full Abstract

Soil hydraulic properties maps (2016) for Europe: for Water retention of topsoil: saturated water content (cm3/cm3), water content at field capacity (cm3/cm3), water content at wilting point (cm3/cm3); for Hydraulic conductivity of topsoil: saturated hydraulic conductivity (cm/day). Besides the true values in the units mentioned values scaled between 1 and 10 without measurement units were also calculated. This layer shows the 'true values' for the water content at wilting point.

2016-01

Predicted distribution of SOC content in Europe (based on LUCAS, BioSoil and CZO) in the context of the EU-funded SoilTrEC project: SOC LUCAS only

These maps of predicted distribution of SOC content in Europe (2016) are based on aggregated 23,835 soil samples collected from the LUCAS Project (samples from agricultural soil), BioSoil Project (...

Full Abstract

These maps of predicted distribution of SOC content in Europe (2016) are based on aggregated 23,835 soil samples collected from the LUCAS Project (samples from agricultural soil), BioSoil Project (samples from forest soil), and Soil Transformations in European Catchments (SoilTrEC) Project (samples from local soil data coming from five different critical zone observatories (CZOs) in Europe).
This layer is SOC LUCAS only.

2016-01

European map of soil suitability to provide a platform for most human activities (EU28)

This dataset (map)(2016) presents the suitability of soil as a platform for most human activities in the EU. Calculation of suitability was done using vaious properties of the European Soil database...

Full Abstract

This dataset (map)(2016) presents the suitability of soil as a platform for most human activities in the EU. Calculation of suitability was done using vaious properties of the European Soil database (soil type, soil water regime, limitation to agricultural use, depth to rock, land use) and slope of the terrain.

2016-01

Maps of the Storing and Filtering Capacity of Soils in Europe: solids and pathogenic microorganisms filtering capacity (FILT_CAPSO)

This dataset consists of a number of data layers (raster GRID maps) that are associated to the peer-reviewed publication "Assessment of soil organic carbon stocks under future climate and land...

Full Abstract

This dataset consists of a number of data layers (raster GRID maps) that are associated to the peer-reviewed publication "Assessment of soil organic carbon stocks under future climate and land cover changes in Europe" . Layers cover the current Soil Organic Carbon Stocks (2016) and the projected Soil Organic Carbon Stocks by 2050, for various Climate Scenarios (CCSM4, HadGEM2-AO , IPSL-CM5A-LR MRI-CGCM3) and Representative Concentration Pathways (RCPs).
hd45: HadGEM2-AO, RCP 4.5

2015-01

Soil erosion by water (RUSLE2015)

Soil erosion by water is one of the major threats to soils in the European Union, with a negative impact on ecosystem services, crop production, drinking water and carbon stocks. The European...

Full Abstract

Soil erosion by water is one of the major threats to soils in the European Union, with a negative impact on ecosystem services, crop production, drinking water and carbon stocks. The European Commission’s Soil Thematic Strategy has identified soil erosion as a relevant issue for the European Union, and has proposed an approach to monitor soil erosion. This paper presents the application of a modified version of the Revised Universal Soil Loss Equation (RUSLE) model (RUSLE2015) to estimate soil loss in Europe for the reference year 2010, within which the input factors (Rainfall erosivity, Soil erodibility, Cover- Management, Topography, Support practices) are modelled with the most recently available pan- European datasets. While RUSLE has been used before in Europe, RUSLE2015 improves the quality of estimation by introducing updated (2010), high-resolution (100 m), peer-reviewed input layers. The mean soil loss rate in the European Union’s erosion-prone lands (agricultural, forests and semi-natural areas) was found to be 2.46 t ha 1 yr 1, resulting in a total soil loss of 970 Mt annually. A major benefit of RUSLE2015 is that it can incorporate the effects of policy scenarios based on land- use changes and support practices. The impact of the Good Agricultural and Environmental Condition (GAEC) requirements of the Common Agricultural Policy (CAP) and the EU’s guidelines for soil protection can be grouped under land management (reduced/no till, plant residues, cover crops) and support practices (contour farming, maintenance of stone walls and grass margins). The policy interventions (GAEC, Soil Thematic Strategy) over the past decade have reduced the soil loss rate by 9.5% on average in Europe, and by 20% for arable lands. Special attention is given to the 4 million ha of croplands which currently have unsustainable soil loss rates of more than 5 t ha 1 yr 1, and to which policy measures should be targeted. More information about the RUSLE2015 model and the data in: Panagos, P., Borrelli, P., Poesen, J., Ballabio, C., Lugato, E., Meusburger, K., Montanarella, L., Alewell, .C. 2015. The new assessment of soil loss by water erosion in Europe. Environmental Science & Policy. 54: 438-447.

2015-01

Support Practices factor (P-factor) for the EU

The USLE/RUSLE support practice factor (P-factor) is rarely taken into account in soil erosion
risk modelling at sub-continental scale, as it is difficult to estimate for large areas. This study...

Full Abstract

The USLE/RUSLE support practice factor (P-factor) is rarely taken into account in soil erosion
risk modelling at sub-continental scale, as it is difficult to estimate for large areas. This study
attempts to model the P-factor in the European Union. For this, it considers the latest policy
developments in the Common Agricultural Policy, and applies the rules set by Member
States for contour farming over a certain slope. The impact of stone walls and grass margins
is also modelled using the more than 226,000 observations from the Land use/cover area
frame statistical survey (LUCAS) carried out in 2012 in the European Union.
The mean P-factor considering contour farming, stone walls and grass margins in the
European Union is estimated at 0.9702. The support practices accounted for in the P-factor
reduce the risk of soil erosion by 3%, with grass margins having the largest impact (57% of the
total erosion risk reduction) followed by stone walls (38%). Contour farming contributes very
little to the P-factor given its limited application; it is only used as a support practice in eight
countries and only on very steep slopes. Support practices have the highest impact in Malta,
Portugal, Spain, Italy, Greece, Belgium, The Netherlands and United Kingdom where they
reduce soil erosion risk by at least 5%. The P-factor modelling tool can potentially be used by
policy makers to run soil-erosion risk scenarios for a wider application of contour farming in
areas with slope gradients less than 10%, maintaining stone walls and increasing the number
of grass margins under the forthcoming reform of the Common Agricultural Policy. More information about the methodology of P-factor estimation in: Panagos, P., Borrelli, P., Meusburger, K., van der Zanden, E.H., Poesen, J., Alewell, C. 2015. Modelling the effect of support practices (P-factor) on the reduction of soil erosion by water at European Scale. Environmental Science & Policy 51: 23-34

2015-01

LS-factor (Slope Length and Steepness factor) for the EU

The Universal Soil Loss Equation (USLE) model is the most frequently used.
model for soil erosion risk estimation. Among the six input layers, the combined slope length
and slope angle (LS-factor...

Full Abstract

The Universal Soil Loss Equation (USLE) model is the most frequently used.
model for soil erosion risk estimation. Among the six input layers, the combined slope length
and slope angle (LS-factor) has the greatest influence on soil loss at the European scale.
The S-factor measures the effect of slope steepness, and the L-factor defines the impact of
slope length. The combined LS-factor describes the effect of topography on soil erosion. The
European Soil Data Centre (ESDAC) developed a new pan-European high-resolution soil
erosion assessment to achieve a better understanding of the spatial and temporal patterns of
soil erosion in Europe. The LS-calculation was performed using the original equation
proposed by Desmet and Govers (1996) and implemented using the System for Automated
Geoscientific Analyses (SAGA), which incorporates a multiple flow algorithm and
contributes to a precise estimation of flow accumulation. The LS-factor dataset was
calculated using a high-resolution (25 m) Digital Elevation Model (DEM) for the whole
European Union, resulting in an improved delineation of areas at risk of soil erosion as
compared to lower-resolution datasets. This combined approach of using GIS software
tools with high-resolution DEMs has been successfully applied in regional assessments in
the past, and is now being applied for first time at the European scale. More information about the LS-factor methodology and the data in: Panagos, P., Borrelli, P., Meusburger, K. 2015. A New European Slope Length and Steepness Factor (LS-Factor) for Modeling Soil Erosion by Water. Geosciences, 5: 117-126

2015-01

Rainfall Erosivity in the EU and Switzerland (R-factor)

Rainfall is one the main drivers of soil erosion. The erosive force of rainfall is expressed as rainfall erosivity. Rainfall erosivity considers the rainfall amount and intensity, and is most...

Full Abstract

Rainfall is one the main drivers of soil erosion. The erosive force of rainfall is expressed as rainfall erosivity. Rainfall erosivity considers the rainfall amount and intensity, and is most commonly expressed as the R-factor in the USLE model and its revised version, RUSLE. At national and continental levels, the scarce availability of data obliges soil erosion modellers to estimate this factor based on rainfall data with only low temporal resolution (daily, monthly, annual averages). The purpose of this study is to assess rainfall erosivity in Europe in the form of the RUSLE R-factor, based on the best available datasets. Data have been collected from 1,541 precipitation stations in all European Union(EU) Member States and Switzerland, with temporal resolutions of 5 to 60 minutes. The R-factor values calculated from precipitation data of different temporal resolutions were normalised to R-factor values with temporal resolutions of 30 minutes using linear regression functions. Precipitation time series ranged from a minimum of 5 years to maximum of 40 years. The average time series per precipitation station is around 17.1 years, the most datasets including the first decade of the 21st century. Gaussian Process Regression(GPR) has been used to interpolate the R-factor station values to a European rainfall erosivity map at 1 km resolution. The covariates used for the R-factor interpolation were climatic data (total precipitation, seasonal precipitation, precipitation of driest/wettest months, average temperature), elevation and latitude/longitude. The mean R-factor for the EU plus Switzerland is 722 MJ mm ha-1 h-1 yr-1, with the highest values (>1,000 MJ mm ha-1 h-1 yr-1) in the Mediterranean and alpine regions and the lowest (<500 MJ mm ha-1 h-1 yr-1) in the Nordic countries. The erosivity density (erosivity normalised to annual precipitation amounts) was also highest in Mediterranean regions which implies high risk for erosive events and floods. More information about the Methodology and the results an be found in : Panagos, P., Ballabio, C., Borrelli, P., Meusburger, K., Klik, A., Rousseva, S., Tadic, M.P., Michaelides, S., Hrabalíková, M., Olsen, P., Aalto, J., Lakatos, M., Rymszewicz, A., Dumitrescu, A., Beguería, S., Alewell, C. Rainfall erosivity in Europe. Sci Total Environ. 511 (2015), pp. 801-814

2015-01

Cover Management factor (C-factor) for the EU

Land use and management influence the magnitude of soil loss. Among the different soil erosion riskfactors, the cover-management factor (C-factor) is the one that policy makers and farmers can...

Full Abstract

Land use and management influence the magnitude of soil loss. Among the different soil erosion riskfactors, the cover-management factor (C-factor) is the one that policy makers and farmers can mostreadily influence in order to help reduce soil loss rates. The present study proposes a methodology forestimating the C-factor in the European Union (EU), using pan-European datasets (such as CORINE LandCover), biophysical attributes derived from remote sensing, and statistical data on agricultural crops andpractices. In arable lands, the C-factor was estimated using crop statistics (% of land per crop) and data onmanagement practices such as conservation tillage, plant residues and winter crop cover. The C-factor innon-arable lands was estimated by weighting the range of literature values found according to fractionalvegetation cover, which was estimated based on the remote sensing dataset Fcover. The mean C-factor inthe EU is estimated to be 0.1043, with an extremely high variability; forests have the lowest mean C-factor(0.00116), and arable lands and sparsely vegetated areas the highest (0.233 and 0.2651, respectively).Conservation management practices (reduced/no tillage, use of cover crops and plant residues) reducethe C-factor by on average 19.1% in arable lands.The methodology is designed to be a tool for policy makers to assess the effect of future land use andcrop rotation scenarios on soil erosion by water. The impact of land use changes (deforestation, arableland expansion) and the effect of policies (such as the Common Agricultural Policy and the push to growmore renewable energy crops) can potentially be quantified with the proposed model. The C-factor dataand the statistical input data used are available from the European Soil Data Centre. More information about the LANDUM model and the data on C-factor in: Panagos, P., Borrelli, P., Meusburger, C., Alewell, C., Lugato, E., Montanarella, L., 2015. Estimating the soil erosion cover-management factor at European scale. Land Use policy 48C: 38-50.

2015-01

Rainfall Erosivity in the EU and Switzerland (Erosivity Density)

Rainfall is one the main drivers of soil erosion. The erosive force of rainfall is expressed as rainfall erosivity. Rainfall erosivity considers the rainfall amount and intensity, and is most...

Full Abstract

Rainfall is one the main drivers of soil erosion. The erosive force of rainfall is expressed as rainfall erosivity. Rainfall erosivity considers the rainfall amount and intensity, and is most commonly expressed as the R-factor in the USLE model and its revised version, RUSLE. At national and continental levels, the scarce availability of data obliges soil erosion modellers to estimate this factor based on rainfall data with only low temporal resolution (daily, monthly, annual averages). The purpose of this study is to assess rainfall erosivity in Europe in the form of the RUSLE R-factor, based on the best available datasets. Data have been collected from 1,541 precipitation stations in all European Union(EU) Member States and Switzerland, with temporal resolutions of 5 to 60 minutes. The R-factor values calculated from precipitation data of different temporal resolutions were normalised to R-factor values with temporal resolutions of 30 minutes using linear regression functions. Precipitation time series ranged from a minimum of 5 years to maximum of 40 years. The average time series per precipitation station is around 17.1 years, the most datasets including the first decade of the 21st century. Gaussian Process Regression(GPR) has been used to interpolate the R-factor station values to a European rainfall erosivity map at 1 km resolution. The covariates used for the R-factor interpolation were climatic data (total precipitation, seasonal precipitation, precipitation of driest/wettest months, average temperature), elevation and latitude/longitude. The mean R-factor for the EU plus Switzerland is 722 MJ mm ha-1 h-1 yr-1, with the highest values (>1,000 MJ mm ha-1 h-1 yr-1) in the Mediterranean and alpine regions and the lowest (<500 MJ mm ha-1 h-1 yr-1) in the Nordic countries. The erosivity density (erosivity normalised to annual precipitation amounts) was also highest in Mediterranean regions which implies high risk for erosive events and floods. More information about the Methodology and the results an be found in : Panagos, P., Ballabio, C., Borrelli, P., Meusburger, K., Klik, A., Rousseva, S., Tadic, M.P., Michaelides, S., Hrabalíková, M., Olsen, P., Aalto, J., Lakatos, M., Rymszewicz, A., Dumitrescu, A., Beguería, S., Alewell, C. Rainfall erosivity in Europe. Sci Total Environ. 511 (2015), pp. 801-814

This map provides a complete picture of the soil erodibility in the European Union member states. It is derived from the LUCAS 2009 point survey (20,000 SOIL SAMPLES) and the European Soil Database...

Full Abstract

This map provides a complete picture of the soil erodibility in the European Union member states. It is derived from the LUCAS 2009 point survey (20,000 SOIL SAMPLES) and the European Soil Database. The K-factor, which expresses the susceptibility of a soil to erode, is related to soil properties such as organic matter content, soil texture, soil structure and permeability. With the Land Use/Cover Area frame Survey (LUCAS) soil survey in 2009 a pan-European soil dataset is available for the first time, consisting of around 20,000 points across 25 Member States of the European Union. The aim of this study is the generation of a harmonised high-resolution soil erodibility map (with a grid cell size of 500 m) for the 25 EU Member States. Soil erodibility was calculated for the LUCAS survey points using the nomograph of Wischmeier and Smith (1978). A Cubist regression model was applied to correlate spatial data such as latitude, longitude, remotely sensed and terrain features in order to develop a high-resolution soil erodibility map. The mean K-factor for Europe was estimated at 0.032 t ha h ha−1 MJ−1 mm−1 with a standard deviation of 0.009 t ha h ha−1 MJ−1 mm−1. The yielded soil erodibility dataset compared well with the published local and regional soil erodibility data. However, the incorporation
of the protective effect of surface stone cover, which is usually not considered for the soil erodibility calculations, resulted in an average 15% decrease of the K-factor. The exclusion of this effect in K-factor calculations is likely to result in an overestimation of soil erosion, particularly for the Mediterranean countries, where highest percentages of surface stone cover were observed. More information about the Methodology and the results an be found in : Panagos P., Meusburger K., Ballabio C., Borrelli P., Alewell C. (2014). Soil erodibility in Europe: A high-resolution dataset based on LUCAS . Science of the Total Environment, 479-480 (1) , pp. 189-200.

2014-01

Soil Erodibility (K- Factor) High Resolution dataset for Europe

This map provides a complete picture of the soil erodibility in the European Union member states. It is derived from the LUCAS 2009 point survey (20,000 SOIL SAMPLES) and the European Soil Database...

Full Abstract

This map provides a complete picture of the soil erodibility in the European Union member states. It is derived from the LUCAS 2009 point survey (20,000 SOIL SAMPLES) and the European Soil Database. The K-factor, which expresses the susceptibility of a soil to erode, is related to soil properties such as organic matter content, soil texture, soil structure and permeability. With the Land Use/Cover Area frame Survey (LUCAS) soil survey in 2009 a pan-European soil dataset is available for the first time, consisting of around 20,000 points across 25 Member States of the European Union. The aim of this study is the generation of a harmonised high-resolution soil erodibility map (with a grid cell size of 500 m) for the 25 EU Member States. Soil erodibility was calculated for the LUCAS survey points using the nomograph of Wischmeier and Smith (1978). A Cubist regression model was applied to correlate spatial data such as latitude, longitude, remotely sensed and terrain features in order to develop a high-resolution soil erodibility map. The mean K-factor for Europe was estimated at 0.032 t ha h ha−1 MJ−1 mm−1 with a standard deviation of 0.009 t ha h ha−1 MJ−1 mm−1. The yielded soil erodibility dataset compared well with the published local and regional soil erodibility data. However, the incorporation
of the protective effect of surface stone cover, which is usually not considered for the soil erodibility calculations, resulted in an average 15% decrease of the K-factor. The exclusion of this effect in K-factor calculations is likely to result in an overestimation of soil erosion, particularly for the Mediterranean countries, where highest percentages of surface stone cover were observed. More information about the Methodology and the results an be found in : Panagos P., Meusburger K., Ballabio C., Borrelli P., Alewell C. (2014). Soil erodibility in Europe: A high-resolution dataset based on LUCAS . Science of the Total Environment, 479-480 (1) , pp. 189-200.

2014-01

Pan-European SOC stock of agricultural soils

Proposed European policy in the agricultural sector will place higher emphasis on soil organic carbon (SOC), both as an indicator of soil quality and as a means to offset CO2 emissions through soil...

Full Abstract

Proposed European policy in the agricultural sector will place higher emphasis on soil organic carbon (SOC), both as an indicator of soil quality and as a means to offset CO2 emissions through soil carbon (C) sequestration. Despite detailed national SOC data sets in several European Union (EU) Member States, a consistent C stock estimation at EU scale remains problematic. Data are often not directly comparable, different methods have been used to obtain values (e.g. sampling, laboratory analysis) and access may be restricted. Therefore, any evolution of EU policies on C accounting and sequestration may be constrained by a lack of an accurate SOC estimation and the availability of tools to carry out scenario analysis, especially for agricultural soils. In this context, a comprehensive model platform was established at a pan-European scale (EU + Serbia, Bosnia and Herzegovina, Croatia, Montenegro, Albania, Former Yugoslav Republic of Macedonia and Norway) using the agro-ecosystem SOC model CENTURY. Almost 164 000 combinations of soil-climate-land use were computed, including the main arable crops, orchards and pasture. The model was implemented with the main management practices (e.g. irrigation, mineral and organic fertilization, tillage) derived from official statistics. The model results were tested against inventories from the European Environment and Observation Network (EIONET) and approximately 20 000 soil samples from the 2009 LUCAS survey, a monitoring project aiming at producing the first coherent, comprehensive and harmonized top-soil data set of the EU based on harmonized sampling and analytical methods. The CENTURY model estimation of the current 0–30 cm SOC stock of agricultural soils was 17.63 Gt; the model uncertainty estimation was below 36% in half of the NUTS2 regions considered. The model predicted an overall increase of this pool according to different climate-emission scenarios up to 2100, with C loss in the south and east of the area (involving 30% of the whole simulated agricultural land) compensated by a gain in central and northern regions. Generally, higher soil respiration was offset by higher C input as a consequence of increased CO2 atmospheric concentration and favourable crop growing conditions, especially in northern Europe. Considering the importance of SOC in future EU policies, this platform of simulation appears to be a very promising tool to orient future policymaking decisions. More information about the model and the data in: Lugato E., Panagos P., Bampa, F., Jones A., Montanarella L. (2014). A new baseline of organic carbon stock in European agricultural soils using a modelling approach. Global change biology. 20 (1), pp. 313-326

2013-02

Pan-European Landslide Susceptibility (ELSUS 1000 version 1)

The European Landslide Susceptibility Map ELSUS 1000 version 1 shows levels of spatial probability of generic landslide occurrence at 1 km cell size. It covers all European Union’s Member States,...

Full Abstract

The European Landslide Susceptibility Map ELSUS 1000 version 1 shows levels of spatial probability of generic landslide occurrence at 1 km cell size. It covers all European Union’s Member States, except Cyprus, and Albania, Bosnia and Herzegovina, Croatia, Kosovo, FYR Macedonia, Montenegro, Norway, Serbia and Switzerland. The map has been produced by regionalizing the study area based on elevation and climatic conditions, followed by spatial multi-criteria evaluation modelling using pan-European slope gradient, soil parent material and land cover spatial datasets as the main landslide conditioning factors. In addition, over 100,000 landslide locations across Europe, provided by various national organizations or collected by the authors, have been used for model calibration and map validation. The map has been made jointly by the Federal Institute for Geosciences and Natural Resources (BGR, Hannover, Germany), the Joint Research Centre (JRC, Ispra, Italy), the Institute of Physics of the Globe (CNRS-EOST, Strasbourg, France), and the Research Institute for Hydrogeological Protection (CNR-IRPI, Perugia, Italy).

2010-01

ph

The JRC created a quantitative map of estimated soil pH values across Europe from a compilation of 12,333 soil pH measurements from 11 different sources, and using a geo-statistical framework based...

Full Abstract

The JRC created a quantitative map of estimated soil pH values across Europe from a compilation of 12,333 soil pH measurements from 11 different sources, and using a geo-statistical framework based on Regression-Kriging. Fifty-four (54) auxiliary variables in the form of raster maps at 1km resolution were used to explain the differences in the distribution of soil pHCaCl2 and the kriged map of the residuals from the regression model was added. The goodness of fit of the regression model was satisfactory (R2adj = 0.43) and its residuals follow a Gaussian distribution. The lowest values correspond to the soils developed on acid rock (granites, quartzite’s, sandstones, etc), while the higher values are related to the presence of calcareous sediments and basic rocks. The validation of the model shows that the model is quite accurate (R2adj = 0.56). This shows the validity of Regression-Kriging in the estimation of the distribution of soil properties when a large and adequately documented number of soil measurements are available.

2004-01

OCTOP: Topsoil Organic Carbon Content for Europe

Soil organic carbon, the major component of soil organic matter, is extremely important in all soil processes. Organic material in the soil is essentially derived from residual plant and animal...

Full Abstract

Soil organic carbon, the major component of soil organic matter, is extremely important in all soil processes. Organic material in the soil is essentially derived from residual plant and animal material, synthesised by microbes and decomposed under the influence of temperature, moisture and ambient soil conditions. The annual rate of loss of organic matter can vary greatly, depending on cultivation practices, the type of plant/crop cover, drainage status of the soil and weather conditions. There are two groups of factors that influence inherent organic matter content: natural factors (climate, soil parent material, land cover and/or vegetation and topography), and human-induced factors (land use, management and degradation).
At the European level, there is a serious lack of geo-referenced, measured and harmonised data on soil organic carbon available from systematic sampling programmes. The European Soil Database, at a scale of 1:1,000,000, is the only comprehensive source of data on the soils of Europe harmonised according to a standard international classification (FAO). At the present time, the most homogeneous and comprehensive data on the organic carbon/matter content of European soils remain those that can be extracted and/or derived from the European Soil Database in combination with associated databases on land cover, climate and topography.
The Soil Portal makes available the Maps of Organic carbon content (%) in the surface horizon of soils in Europe. The data are in ESRI GRID format and are available as an ASCII raster file or in native ESRI GRID format. In addition, an interactive application allows the user to navigate in the Organic Carbon data with OCTOP Map Server and print his own customized map. The list of authorised codes and their corresponding meanings is given in the following table:
Organic carbon content (%)
Value Ranges
--------------
0 - 0.01
0.01 - 1.00
1.0 - 2.0
2.0 - 6.0
6.0 - 12.5
12.5 - 25.0
25.0 - 35.0
> 35.0

2003-01

PESERA: Pan European Soil Erosion Risk Assessment

The Pan-European Soil Erosion Risk Assessment - PESERA - uses a process-based and spatially distributed model to quantify soil erosion by water and assess its risk across Europe. The conceptual basis...

Full Abstract

The Pan-European Soil Erosion Risk Assessment - PESERA - uses a process-based and spatially distributed model to quantify soil erosion by water and assess its risk across Europe. The conceptual basis of the PESERA model can also be extended to include estimates of tillage and wind erosion. The model is intended as a regional diagnostic tool, replacing comparable existing methods, such as the Universal Soil Loss Equation (USLE), which are less suitable for European conditions and lack compatibility with higher resolution models.

2001-01

BS_TOP: Base Topsoil Saturation

BS_TOP: Base saturation of the topsoil
The list of authorised codes and their corresponding meanings is given in the following table:
BS_TOP: Base saturation of the topsoil)
Code Value...

WM1:Presence of Water Management System
WM1 : Code for normal presence and purpose of an existing water management system in agricultural land on more than 50% of the STU.
A water management...

Full Abstract

WM1:Presence of Water Management System
WM1 : Code for normal presence and purpose of an existing water management system in agricultural land on more than 50% of the STU.
A water management system is intended to palliate the lack of water (dry conditions), correct a soil condition preventing agricultural use (salinity), or drain excess water in waterlogged or frequently flooded areas. In some cases, it has a double purpose, for example in zones with contrasting seasonal conditions, alternatively flooded or experiencing droughts. The most obvious, apparent, or dominant type of water management system must be chosen from the list according to the contributor's expertise.
Obviously, WM1 and WM2 are inter-dependant. For example, if WM1 = 2 (no water management system) then WM2 can only have value 2. As another example, WM1 = 3 (drainage) is clearly incompatible with WM2 = 9 (flooding).
The list of authorised codes and their corresponding meanings is given in the following tables for attributes WM1:
WM1 : Code for normal presence and purpose of an existing water management system in agricultural land on more than 50% of the STU
Code Value
--------------
0 No information
1 Not applicable (no agriculture)
2 No water management system
3 A water management system exists to alleviate
waterlogging (drainage)
4 A water management system exists to alleviate
drought stress (irrigation)
5 A water management system exists to alleviate
salinity (drainage)
6 A water management system exists to alleviate both
waterlogging and drought stress
7 A water management system exists to alleviate both
waterlogging and salinity

2001-01

USESE:Secondary Land Use

USESE:Secondary Land Use
USE-SEC : Code for Secondary land use of the STU Land Use
USE DOM describes the dominant and most apparent land use for an STU. A second type of land use can be taken...

Full Abstract

USESE:Secondary Land Use
USE-SEC : Code for Secondary land use of the STU Land Use
USE DOM describes the dominant and most apparent land use for an STU. A second type of land use can be taken into account in USE SEC. The map co-ordinator must use his expert judgement to determine what are the dominant and secondary land uses for an STU, as the soil can cover extensive surfaces in regions with different agricultural practices and crops.
If there is only one land use or if the variability is unknown, then the value of USE DOM must be copied to USE SEC. Land uses that do not involve much human intervention, such as wasteland, or wildlife refuse, or land above timberline, are also listed here. The list of authorised codes and their corresponding meanings is given in the following table for attributes USE-SEC :
USE-SEC : Code for Secondary land use of the STU Land Use
Code Value
--------------
0 No information
1 Pasture, grassland, grazing land
2 Poplars
3 Arable land, cereals
4 Wasteland, shrub
5 Forest, coppice
6 Horticulture
7 Vineyards
8 Garrigue
9 Bush, macchia
10 Moor
11 Halophile grassland
12 Arboriculture, orchard
13 Industrial crops
14 Rice
15 Cotton
16 Vegetables
17 Olive trees
18 Recreation
19 Extensive pasture, grazing, rough pasture
20 Dehesa (extensive pastoral system in forest parks
in Spain)
21 Cultivos enarenados (artificial soils for orchards
in SE Spain)
22 Wildlife refuge, land above timberline

2001-01

STR_TOP: Topsoil structure

STR_TOP:Topsoil structure
The list of authorised codes and their corresponding meanings is given in the following table:
STR_TOP:Topsoil structure
Code Value
--------------
G = Good
N...

TXSRFSE:Secondary surface textural class
A Soil Typological Unit (STU) can have surface textures that fall in two different textural classes. The secondary surface textural class (TXSRFSE) is used...

Full Abstract

TXSRFSE:Secondary surface textural class
A Soil Typological Unit (STU) can have surface textures that fall in two different textural classes. The secondary surface textural class (TXSRFSE) is used to indicate the surface texture less extensive than the dominant one. Together the TXSRFDO and the TXSRFSE (Sec.surface text.class) attributes describe the lateral variability of the surface horizon texture within the STU. If there is no such variability or if information is unavailable, then the value of TXSRFDO must also be entered for TXSRFSE .
TEXT-SRF-SEC: Secondary surface textural class of the STU
----------------
0 No information
9 No mineral texture (Peat soils)
1 Coarse (18% < clay and > 65% sand)
2 Medium (18% < clay < 35% and >= 15% sand, or 18% < clay and 15% < sand < 65%)
3 Medium fine (< 35% clay and < 15% sand)
4 Fine (35% < clay < 60%)
5 Very fine (clay > 60 %)

2001-01

WM2: Type of Water Management System

WM2:Type of Water Management System
WM2: Code for the type of an existing water management system.
A water management system is intended to palliate the lack of water (dry conditions), correct a...

Full Abstract

WM2:Type of Water Management System
WM2: Code for the type of an existing water management system.
A water management system is intended to palliate the lack of water (dry conditions), correct a soil condition preventing agricultural use (salinity), or drain excess water in waterlogged or frequently flooded areas. In some cases, it has a double purpose, for example in zones with contrasting seasonal conditions, alternatively flooded or experiencing droughts. The most obvious, apparent, or dominant type of water management system must be chosen from the list according to the contributor's expertise.
Obviously, WM1 and WM2 are inter-dependant. For example, if WM1 = 2 (no water management system) then WM2 can only have value 2. As another example, WM1 = 3 (drainage) is clearly incompatible with WM2 = 9 (flooding).
The list of authorised codes and their corresponding meanings is given in the following tables for attributes WM2:
WM2: Code for the type of an existing water management system
Code Value
--------------
0 No information
1 Not applicable (no agriculture)
2 No water management system
3 Pumping
4 Ditches
5 Pipe under drainage (network of drain pipes)
6 Mole drainage
7 Deep loosening (subsoiling)
8 'Bed' system (ridge-funow or steching)
9 Flood irrigation (system of irrigation by
controlled flooding as for rice)
10 Overhead sprinkler (system of irrigation by
sprinkling)
11 Trickle irrigation

2001-01

HG: Hydro-geological class

HG:Hydro-geological class
The list of authorised codes and their corresponding meanings is given in the following table:
HG:Hydro-geological class
Code Value
--------------
1R = 1R
1C...

ROO:Depth Class of obstacle to roots
ROO: Depth class of an obstacle to roots within the STU
An obstacle to roots is defined as a subsoil horizon restricting root penetration. It can be of...

Full Abstract

ROO:Depth Class of obstacle to roots
ROO: Depth class of an obstacle to roots within the STU
An obstacle to roots is defined as a subsoil horizon restricting root penetration. It can be of lithologic origin (lithic contact), or pedogenic origin (fragipan, duripan, petrocalcic or petroferric horizons), or can result from the accumulation of toxic elements, or from waterlogging.
The ROO attribute holds the depth class of an obstacle to roots within the STU. The list of authorised codes and their corresponding meanings is given in the following table for attribute ROO:
ROO: Depth class of an obstacle to roots within the STU
Code Value
--------------
0 No information
1 No obstacle to roots between 0 and 80 cm
2 Obstacle to roots between 60 and 80 cm depth
3 Obstacle to roots between 40 and 60 cm depth
4 Obstacle to roots between 20 and 40 cm depth
5 Obstacle to roots between 0 and 80 cm depth
6 Obstacle to roots between 0 and 20 cm depth

2001-01

EAWC_SUB: Subsoil easily available water capacity

EAWC_SUB: Subsoil easily available water capacity.
The list of authorised codes and their corresponding meanings is given in the following table:
EAWC_SUB: Subsoil easily available water...

ZMIN: Minimum elevation above sea level of the STU (in metres).
It is often difficult to fill in the information concerning ZMIN and ZMAX attributes. This is particularly true when the map coverage...

Full Abstract

ZMIN: Minimum elevation above sea level of the STU (in metres).
It is often difficult to fill in the information concerning ZMIN and ZMAX attributes. This is particularly true when the map coverage is a generalisation of previous maps that were not very detailed themselves. The use of a Digital Elevation Model (DEM) will often palliate the lack of information for these attributes. Using a DEM will however apply to the whole Soil Mapping Unit, and not to the individual STU components, as required here. Hence the information should be provided if available from the soil survey. The range of authorised values for attributes ZMIN and ZMAX is an integer number selected in [ 999, and the interval -400 ,9999]. –999 is the code used when no information is available. The negative values in the interval are given since some areas, such as the Caspian or Dead Seas, are below the general ocean level. The following table holds the coding scheme for attributes ZMIN:
ZMIN: Minimum elevation above sea level of the STU (in metres)
values
--------------
-999 No information
-2 metres
-1 metres
0 metres
1 metres
2 metres
5000 metres

2001-01

STR_SUB: Subsoil structure

STR_SUB:Subsoil structure
The list of authorised codes and their corresponding meanings is given in the following table:
STR_SUB:Subsoil structure
Code Value
--------------
G = Good...

PD_TOP:Topsoil packing density
The list of authorised codes and their corresponding meanings is given in the following table:
PD_TOP:Topsoil packing density
Code Value
--------------
L...

Full Abstract

PD_TOP:Topsoil packing density
The list of authorised codes and their corresponding meanings is given in the following table:
PD_TOP:Topsoil packing density
Code Value
--------------
L = Low
M = Medium
H = High

2001-01

USEDO:Dominant Land Use

USEDO:Dominant Land Use
USE-DOM: Code for dominant land use of the STU.
USE DOM describes the dominant and most apparent land use for an STU. A second type of land use can be taken into account...

Full Abstract

USEDO:Dominant Land Use
USE-DOM: Code for dominant land use of the STU.
USE DOM describes the dominant and most apparent land use for an STU. A second type of land use can be taken into account in USE SEC. The map co-ordinator must use his expert judgement to determine what are the dominant and secondary land uses for an STU, as the soil can cover extensive surfaces in regions with different agricultural practices and crops.
If there is only one land use or if the variability is unknown, then the value of USE DOM must be copied to USE SEC. Land uses that do not involve much human intervention, such as wasteland, or wildlife refuse, or land above timberline, are also listed here. The list of authorised codes and their corresponding meanings is given in the following table for attributes USE-DOM :
USE-DOM: Code for dominant land use of the STU
Code Value
--------------
0 No information
1 Pasture, grassland, grazing land
2 Poplars
3 Arable land, cereals
4 Wasteland, shrub
5 Forest, coppice
6 Horticulture
7 Vineyards
8 Garrigue
9 Bush, macchia
10 Moor
11 Halophile grassland
12 Arboriculture, orchard
13 Industrial crops
14 Rice
15 Cotton
16 Vegetables
17 Olive trees
18 Recreation
19 Extensive pasture, grazing, rough pasture
20 Dehesa (extensive pastoral system in forest parks
in Spain)
21 Cultivos enarenados (artificial soils for orchards
in SE Spain)
22 Wildlife refuge, land above timberline

2001-01

WR: Annual average soil water regime

WR:Annual average soil water regime
Dominant annual average soil water regime class of the soil profile of the STU.
The annual average soil water regime is an estimate of the soil moisture...

Full Abstract

WR:Annual average soil water regime
Dominant annual average soil water regime class of the soil profile of the STU.
The annual average soil water regime is an estimate of the soil moisture conditions throughout the year. It is based on time series of matrix suction profiles, or groundwater table depths, or soil morphological attributes, or a combination of these characteristics. The annual soil water regime is expressed in terms of the duration of the state of soil wetness during the year. A soil is wet when it is saturated and has a matrix suction less than 10 cm, or a matrix potential over 1 kPa. Time is counted in cumulative days and not as successive days of wet conditions.
“Wet” means waterlogged and is defined as: a matrix suction of less than 10 cm, or a matrix potential over 1 kPa. The WR attribute is used to describe the dominant annual average soil water regime class of the soil profile of the STU. The list of authorised codes and their corresponding meaning is given in the following table for attribute WR:
Dominant annual average soil water regime class of the soil profile of the STU
Code Value
--------------
0 No information
1 Not wet within 80 cm for over 3 months, nor wet
within 40 cm for over 1 month
2 Wet within 80 cm for 3 to 6 months, but not wet
within 40 cm for over 1 month
3 Wet within 80 cm for over 6 months, but not wet
within 40 cm for over 11 months
4 Wet within 40 cm depth for over 11 months

2001-01

Natural Soil Susceptibility to Compaction

The map of natural soil susceptibility to compaction was created from the evaluation of selected parameters from the European Soil Database. The soil susceptibility to compaction was divided into 4...

Full Abstract

The map of natural soil susceptibility to compaction was created from the evaluation of selected parameters from the European Soil Database. The soil susceptibility to compaction was divided into 4 categories. Two additional categories represent the data concerning places where this evaluation was either not relevant or could not been provided because of lack of information. In total there are 6 categories:
Natural Soil Susceptibility to Compaction
Code Value
--------------
0 - no soil. This represents water bodies, glaciers and rock outcrops
1 - low susceptibility to compaction
2 - medium susceptibility to compaction
3 - high susceptibility to compaction
4 - very high susceptibility to compaction
9 - no evaluation possible. This was the case of towns including also soils, soils disturbed by man and marsh.

2001-01

TXTCRUST: Textural factor of soil crusting

TXTCRUST:Textural factor of soil crusting
The list of authorised codes and their corresponding meanings is given in the following table:
TXTCRUST: Textural factor of soil crusting
Code Value...

USE:Regrouped land use class.
The list of authorised codes and their corresponding meanings is given in the following table:
USE:Regrouped land use class.
Code Value
--------------
HG...

Full Abstract

USE:Regrouped land use class.
The list of authorised codes and their corresponding meanings is given in the following table:
USE:Regrouped land use class.
Code Value
--------------
HG = Halophile Grassland
MG = Managed Grassland
SN = Semi-natural
C = Cultivated
# = No information

2001-01

ZMAX:Maximun elevetaion above sea

ZMAX: Maximum elevation above sea level of the STU (in metres).
It is often difficult to fill in the information concerning ZMIN and ZMAX attributes. This is particularly true when the map coverage...

Full Abstract

ZMAX: Maximum elevation above sea level of the STU (in metres).
It is often difficult to fill in the information concerning ZMIN and ZMAX attributes. This is particularly true when the map coverage is a generalisation of previous maps that were not very detailed themselves. The use of a Digital Elevation Model (DEM) will often palliate the lack of information for these attributes. Using a DEM will however apply to the whole Soil Mapping Unit, and not to the individual STU components, as required here. Hence the information should be provided if available from the soil survey. The range of authorised values for attributes ZMIN and ZMAX is an integer number selected in [ 999, and the interval -400 ,9999]. –999 is the code used when no information is available. The negative values in the interval are given since some areas, such as the Caspian or Dead Seas, are below the general ocean level. The following table holds the coding scheme for attributes ZMAX:
ZMAX: Maximum elevation above sea level of the STU (in metres)
values
--------------
-999 No information
-2 metres
-1 metres
0 metres
1 metres
2 metres
5000 metres

2001-01

TEXT: Dominant Surface Textural

TEXT: Dominant surface textural class (completed from dominant STU)
The list of authorised codes and their corresponding meanings is given in the following table:
TEXT: Dominant surface...

AGLIM1:Dominant limitation to agricultural use
AGLIM1: Dominant limitation to agricultural use.
A STU can have more than one limitation for agricultural use. Only the two most important...

Full Abstract

AGLIM1:Dominant limitation to agricultural use
AGLIM1: Dominant limitation to agricultural use.
A STU can have more than one limitation for agricultural use. Only the two most important limitations are considered and ranked in order of their relative importance. Attribute AGLIM1 contains the code of the most important limitation and attribute AGLIM2 the code of the secondary limitation. If there is only one limitation or if the secondary limitation is unknown, then the value of AGLIM1 must also be entered for AGLIM2 . For example, a soil can be both shallow, with a lithic contact within the first 50 cm, and have more than 35% gravel. The pedologist may determine that shallowness is the dominant limiting factor and gravel content is the secondary limitation. Recently, duripans and petroferric horizons have been added to the list of limiting factors. These horizons are more often found in soils of the Mediterranean area than in northern Europe. The major types of chemical and physical limitations for agricultural use are listed below. Most limitations listed here, however, are physical. The list of authorised codes and their corresponding meanings is given in the following table for attribute AGLIM1:
AGLIM1: Dominant limitation to agricultural use
Code Value
--------------
0 No information
1 No limitation to agricultural use
2 Gravelly (over 35% gravel diameter < 7.5 cm)
3 Stony (presence of stones diameter > 7.5 cm,
impracticable mechanisation)
4 Lithic (coherent and hard rock within 50 cm)
5 Concretionary (over 35% concretions diameter < 7.5
cm near the surface)
6 Petrocalcic (cemented or indurated calcic horizon
within 100 cm)
7 Saline (electric conductivity > 4 mS.cm-1 within
100 cm)
8 Sodic (Na/T > 6% within 100 cm)
9 Glaciers and snow-caps
10 Soils disturbed by man (i.e. landfills, paved
surfaces, mine spoils)
11 Fragipans
12 Excessively drained
13 Almost always flooded
14 Eroded phase, erosion
15 Phreatic phase (shallow water table)
16 Duripan (silica and iron cemented subsoil horizon)
17 Petroferric horizon
18 Permafrost

2001-01

PARMADO:Dominant Parent Material

PARMADO:Dominant Parent Material
The parent material code must be selected from the list provided below. This list has evolved from a number of approximations using experiences from several...

A Soil Typological Unit (STU) can have surface textures that fall in two different textural classes. The secondary surface textural class (TXSRFSE) is used to indicate the surface texture less...

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A Soil Typological Unit (STU) can have surface textures that fall in two different textural classes. The secondary surface textural class (TXSRFSE) is used to indicate the surface texture less extensive than the dominant one. Together the TXSRFDO and the TXSRFSE (Sec.surface text.class) attributes describe the lateral variability of the surface horizon texture within the STU. If there is no such variability or if information is unavailable, then the value of TXSRFDO must also be entered for TXSRFSE .
The list of authorised codes and their corresponding meanings is given in the following table:
TEXT-SRF-DOM: Dominant surface textural class of the STU
Code Value
----------------
0 No information
9 No mineral texture (Peat soils)
1 Coarse (18% < clay and > 65% sand)
2 Medium (18% < clay < 35% and >= 15% sand, or 18% <
clay and 15% < sand < 65%)
3 Medium fine (< 35% clay and < 15% sand)
4 Fine (35% < clay < 60%)
5 Very fine (clay > 60 %)

2001-01

WRB-ADJ1: First soil adjective code

The European Soil Database
WRBFU:Full soil Code
Soil Adjective code of the STU taken from the World Reference Base (WRB) for Soil Resources.
WRB-ADJ1: First soil adjective code of the STU...

TXSUBDO:Dominant sub-surface textural class
A Soil Typological Unit (STU) can have contrasted sub-surface textures that fall in two different textural classes. The secondary sub-surface textural...

Full Abstract

TXSUBDO:Dominant sub-surface textural class
A Soil Typological Unit (STU) can have contrasted sub-surface textures that fall in two different textural classes. The secondary sub-surface textural class (TEXT-SUB-SEC) is used to indicate which sub-surface texture is less extensive than the dominant one. Together the TEXT-SUB-DOM and the TEXT-SUB-SEC attributes reflect the lateral variability of the sub-surface horizon texture within the STU. If there is no such variability or if there is no information, the value of TEXT-SUB-DOM must also be entered for TEXT-SUB-SEC.
>TEXT-SUB-DOM: Dominant sub-surface textural class of the STU
----------------
0 No information
9 No mineral texture (Peat soils)
1 Coarse (18% < clay and > 65% sand)
2 Medium (18% < clay < 35% and >= 15% sand, or 18% < clay and 15% < sand < 65%)
3 Medium fine (< 35% clay and < 15% sand)
4 Fine (35% < clay < 60%)
5 Very fine (clay > 60 %)

2001-01

TXDEPCHG:Depth class to a textural change

TXDEPCHG:Depth class to a textural change
If a textural contrast is present within the soil profile, then this change of textural class for the STU must be recorded in attribute TEXT-DEP-CHG....

Full Abstract

TXDEPCHG:Depth class to a textural change
If a textural contrast is present within the soil profile, then this change of textural class for the STU must be recorded in attribute TEXT-DEP-CHG. The textural contrast is recorded for both areas with dominant and secondary surface textures, when necessary.
TEXT-DEP-CHG: Depth class to a textural change of the dominant and/or secondary surface texture of the STU
----------------
0 No information
1 Textural change between 20 and 40 cm depth
2 Textural change between 40 and 60 cm depth
3 Textural change between 60 and 80 cm depth
4 Textural change between 80 and 120 cm depth
5 No textural change between 20 and 120 cm depth
6 Textural change between 20 and 60 cm depth
7 Textural change between 60 and 120 cm depth

2001-01

SLOPESE: Secondary Slope class

SLOPE-SEC: Secondary Slope class of the STU.
The SLOPE SEC attribute provides an option to indicate a secondary slope class when slope variability within an STU is important and some parts of the...

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SLOPE-SEC: Secondary Slope class of the STU.
The SLOPE SEC attribute provides an option to indicate a secondary slope class when slope variability within an STU is important and some parts of the STU fall into a different slope class than that of the dominant one. If there is no variability or if the variability is unknown, the value of SLOPE DOM must be copied to SLOPE SEC . The list of authorised codes and their corresponding meanings is given in the following tables for attributes SLOPE SEC:
SLOPE-SEC: Secondary Slope class of the STU
Code Value
--------------
0 No information
1 Level (dominant slope ranging from 0 to 8 %)
2 Sloping (dominant slope ranging from 8 to 15 %)
3 Moderately steep (dominant slope ranging from 15
to 25 %)
4 Steep (dominant slope over 25 %)

2001-01

ERODI: Soil erodibility class

ERODI:Soil erodibility class
The list of authorised codes and their corresponding meanings is given in the following table:
ERODI:Soil erodibility class
Code Value
--------------
1 =...

The Saline and Sodic Soils Map shows the area distribution of saline, sodic and potentially salt affected areas within the European Union. The accuracy of input input data only allows the designation...

Full Abstract

The Saline and Sodic Soils Map shows the area distribution of saline, sodic and potentially salt affected areas within the European Union. The accuracy of input input data only allows the designation of salt affected areas with a limited level of reliability (e.g. < 50 or > 50% of the area); therefore the results represented in the map should only be used for orientating purposes.In total there are 5 categories:
Saline and Sodic Soils
Code Value
--------------
1 - Saline > 50% of the area
2 - Sodic > 50% of the area
3 - Saline < 50% of the area
4 - Sodic < 50% of the area
5 - Potentially salt affected soils.

2001-01

IL: Impermeable Layer

IL:Impermeable Layer
IL: Code for the presence of an impermeable layer within the soil profile of the STU
An impermeable layer is a subsoil horizon restricting water penetration. The...

Full Abstract

IL:Impermeable Layer
IL: Code for the presence of an impermeable layer within the soil profile of the STU
An impermeable layer is a subsoil horizon restricting water penetration. The impermeability can be of lithological origin (lithic contact), or pedogenic origin (claypan, duripan, petrocalcic or petroferric horizons,…). The IL attribute holds the code for the presence of an impermeable layer within the soil profile.
The list of authorised codes and their corresponding meaning is given in the following table for attribute IL:
IL: Code for the presence of an impermeable layer within the soil profile of the STU
Code Value
--------------
0 No information
1 No impermeable layer within 150 cm
2 Impermeable layer between 80 and 150 cm
3 Impermeable layer between 40 and 80 cm
4 Impermeable layer within 40 cm

2001-01

MIN_SUB: Subsoil Mineralogy

MIN_SUB:Subsoil Mineralogy
The list of authorised codes and their corresponding meanings is given in the following table:
MIN_SUB:Subsoil Mineralogy
Code Value
--------------
KQ = 1/1...

TXSUBSE:Sec.Sub-surface text.class
A Soil Typological Unit (STU) can have contrasted sub-surface textures that fall in two different textural classes. The secondary sub-surface textural class (...

Full Abstract

TXSUBSE:Sec.Sub-surface text.class
A Soil Typological Unit (STU) can have contrasted sub-surface textures that fall in two different textural classes. The secondary sub-surface textural class (TEXT-SUB-SEC) is used to indicate which sub-surface texture is less extensive than the dominant one. Together the TEXT-SUB-DOM and the TEXT-SUB-SEC attributes reflect the lateral variability of the sub-surface horizon texture within the STU. If there is no such variability or if there is no information, the value of TEXT-SUB-DOM must also be entered for TEXT-SUB-SEC.
TEXT-SUB-SEC :Secondary sub-surface textural class of the STU
----------------
0 No information
9 No mineral texture (Peat soils)
1 Coarse (18% < clay and > 65% sand)
2 Medium (18% < clay < 35% and >= 15% sand, or 18% <
clay and 15% < sand < 65%)
3 Medium fine (< 35% clay and < 15% sand)
4 Fine (35% < clay < 60%)
5 Very fine (clay > 60 %)